Worldwide, cardiovascular diseases (CVD) are among the leading causes of mortality and morbidity with ever-increasing prevalence. CVD is used to classify numerous conditions that affect the heart, heart valves, blood, and vasculature of the body. One of these conditions is coronary artery disease (CAD). Statins are a family of cholesterol lowering drugs for people at high risk of cardiovascular complications. Statins are widely used, as alone in the USA there are almost 20 million statin treated patients and it has been calculated that some 50 million patients would benefit of statin treatment in the USA. However, despite statin treatment the CVD patients have risk to develop severe CVD complications. Early targeted initiation of preventive measures of CVD-related fatal complications, such as acute myocardial infarction (AMI) and death, would be of great benefit and can provide a major opportunity in reducing mortality and morbidity in patients suffering from CVD. To this end, accurate identification of individuals who are at risk of developing CVD complications is essential. However, traditional risk assessment fails to recognize a substantial proportion of patients at high risk while a large proportion of individuals are classified as having intermediate risk, leaving patient management uncertain. Additional strategies to further refine risk assessment of high-risk CVD are therefore highly needed. To this end, the inventors have evaluated the role of novel lipidomic biomarkers as a prognostic tool for fatal cardiovascular events in CVD patients.
Statins are widely used drugs to prevent atherosclerotic end points in CVD patients and, therefore, a significant portion of middle aged population is being treated with statins. Statins do lower efficiently LDL-cholesterol and also many other lipids in the circulation. Thus, statin treatment is significantly affecting plasma concentrations of many potential lipidomic markers and therefore it is important to separately study lipidomic biomarkers in subjects on statin treatment and without statin treatment. It is known in the clinical practice that conventional lipid biomarkers such as LDL-cholesterol are not informative in statin treated patients, but these patients may still have a substantial residual risk of CAD complications despite statin treatment. This current invention deals with subjects who are not undergoing statin treatment at the time of the risk evaluation. A novel innovative aspect here is that the investigators are studying risk markers separately in patients with type 2 Diabetes (DM2). DM2 is causing numerous metabolic alterations in the human body and, therefore, DM2 may affect plasma levels of lipidomic biomarkers as well. Furthermore, CVD risk lipids in non-DM2 patients and DM2 patients may not be the same and the prognostic accuracy can potentially be greatly improved if these subject groups are studied separately.
Plasma or serum total cholesterol, LDL-cholesterol or HDL-cholesterol concentrations have been used as gold standard biomarkers for CVD/CAD risk prediction. However, a number of coronary artery disease (CAD) or acute myocardial infarction (AMI) patients have LDL-C levels within the recommended range suggesting the need for additional diagnostic measures of the residual risk. It is evident from earlier large scale population studies that these measurements associate with the CAD risk and CAD endpoints such as AMI or cardiovascular death. Therefore, preventive treatment strategies have so far been addressed to lower LDL-C concentrations (mainly by statin treatment) and more recently also attempts to raise HDL-C have been made (e.g., by CETP-inhibitors). On the other hand, it has also been observed that one half of the AMI patients actually do have normal LDL cholesterol levels and that there is a substantial residual risk in statin treated patients despite a LDL-C lowering. Furthermore, recent publications have demonstrated that plasma levels of apolipoprotein B (apoB), the main surface protein on LDL particles, and LDL-C, the amount of cholesterol in those particles, are correlated and, considered separately, as positive risk factors. Plasma levels of apolipoprotein A1, the main surface protein on HDL particles, and HDL-C, the amount of cholesterol in those particles, are also correlated with each other and, considered separately, as negative risk factors. Importantly, for a given usual apoB, lower LDL-C has been observed to associate with a higher risk of AMI supporting the view that, on average, LDL particles with low cholesterol content per particle (small, dense LDL particles) are particularly hazardous. Thus, it seems possible that LDL-C associates directly with the more dangerous molecules carried by LDL-particles and that LDL-C is only an indirect measurement of the risk. Therefore, it is of importance to search for molecules e.g., certain lipid species that are directly related with hazardous (i.e., fatal) cardiovascular events.
Lipid metabolite imbalance is a probable cause of dyslipidemia and the ensuing atherosclerosis manifested in its gravest form as the vulnerable atherosclerotic plaque. Atherosclerotic plaques are complex molecular formations that contain numerous lipids. However, there are other factors than lipid rich plaques or LDL cholesterol that make lipids an attractive group of molecules for CVD studies. Lipids are tightly regulated which makes Lipidomic data robust and informative on the current state of the studied organism. Also, lipids are one of the culmination points of a biological system, more the true outcome than the predictor. Combining Lipidomic data with appropriate biobanked clinical material presents a good opportunity for biomarker discovery. Moreover, lipidomics can be used as a gauge of efficacy and safety in drug development and evolving theragnostics. Lipidomic biomarkers are prime candidates for true companion diagnostics in the CVD area and present many opportunities for improved translational medicine as well.
The plaque building blocks and lipoprotein components that are thought to traffic lipids to the site of lesion formation can now be resolved with Lipidomic studies correlating lipid structure and composition to function and thereby disease pathogenesis. The number of lipid mediators in the human body is overwhelming. Their identification and quantification is facilitated by the advances in mass spectrometry and lipid biochemistry, which enable the simultaneous high throughput identification and quantification of hundreds of molecular lipid species in several lipid classes (Ejsing C S, et al: Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proc Natl Acad Sci USA 2009, 106:2136-2141; Stahlman M, et al: High-throughput shotgun lipidomics by quadrupole time-of-flight mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2009 Hiukka A, et al: ApoCIII-enriched LDL in type 2 diabetes displays altered lipid composition, increased susceptibility for sphingomyelinase, and increased binding to biglycan. Diabetes 2009, 58:2018-2026; Linden D, et al: Liver-directed overexpression of mitochondrial glycerol-3-phosphate acyltransferase results in hepatic steatosis, increased triacylglycerol secretion and reduced fatty acid oxidation. FASEB J 2006, 20:434-443.) collectively referred to as the lipidome. Lipidomic studies identify lipid cellular distribution and describe their biochemical mechanisms, interactions and dynamics. Importantly, lipidomics quantifies the exact chemical composition of lipidomes (Han X, Gross R W: Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J Lipid Res 2003, 44:1071-1079).
Due to both high sensitivity and selectivity of lipidomics, even the smallest sample amounts can be analyzed today. The bulk of the lipid data in the art today presents lipids in a sum composition format, i.e. phosphatidylcholine (PC) 34:1 (Brugger B, et al: Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionization tandem mass spectrometry. Proc Natl Acad Sci USA 1997, 94:2339-2344) where the molecular lipid and the attached fatty acid tails remain unidentified. The identification of molecular lipid species, e.g., PC 16:0/18:1 (Ekroos K, et al: Charting molecular composition of phosphatidylcholines by fatty acid scanning and ion trap MS3 fragmentation. J Lipid Res 2003, 44:2181-2192) is the main feature of advanced lipidomics, which delivers highly resolved molecular lipid species rather than summed fatty acid information. For example, the information of the type of fatty acids and their positions of attachment to the glycerol backbone making up the particular PC molecule is revealed. There are conventional techniques such as thin-layer chromatography combined with gas chromatography but they not only require considerably larger sample amounts and laborious sample preparation, but they do not deliver the molecular lipid species. Despite multiple mass spectrometry techniques capable of characterizing lipid entities, most of them are still unable to deliver reliable high-quality quantitative data in terms of absolute or close-to absolute concentrations. In the context of the present invention, electrospray ionization mass spectrometry-based lipidomics is the preferred technology and can utilize both shotgun and targeted lipidomics for exhaustive deciphering and precise quantification of molecular lipidomes. The superior quality and specificity of shotgun and targeted lipidomics will meet stringent regulatory standards, such as good laboratory practice guidelines (GLP) when set-up in the proper environment. Using these technologies quantification of up to two thousand molecular lipids is possible even in a high throughput format.
Lipidomics is a tool for differentiating patients based on their molecular lipid profiles. Personalized medicine and diagnostics enabled by lipidomics will facilitate the mission of the right individual receiving the right drug at the right time and dose. Several works employing analytes consisting of lipids, proteins and hydrophilic molecules among many others have been conducted to meet the needs of personalized medicine. Recently, non-hypothesis-driven metabolomic screenings have been used to identify novel CVD biomarkers.
For example, WO2004/038381 discloses a method for metabolomically facilitating the diagnosis of a disease state of a subject, or for predicting whether a subject is predisposed to having a disease state wherein the small molecule profile from a subject is obtained and compared to a standard small molecule profile.
WO2008/148857 discloses a method to assess the risk of cardiovascular disease in a patient (including atherosclerosis) by isolating the HDL fraction and sub-fraction from a blood sample of the patient. The components of the HDL fraction or sub-fraction to be measured were Sphingosine-1-Phosphate (S1P), sphingomyelin (SM) and Apolipoprotein A-I (apoA-1).
WO2008/11943 further discloses markers for detecting coronary artery disease that can indicate a patient at risk of having or developing coronary artery disease. These include 15 “first-choice” molecules which were: C18:3 Cholesterol ester, C32:1 Phosphatidylcholine, Alanine, Lipid (mainly VLDL), Lysine, Hexadecanoic acid, C36:2 Phosphatidylcholine, Formate, C32:2 Phosphatidylcholine, C18:2 (Linoleic Acid), Cholesterol, C18:2 Lyso-phosphatidylcholine, C36:3 Phosphatidylcholine, C34:4 Phosphatidylcholine and C34:3 Phosphatidylcholine.
Furthermore, US2007/0099242 describes a method to determine if a subject is at risk to develop, or is suffering from cardiovascular disease. The method involves determining a change in the amount of a biomarker in the biological sample or HDL sub-fraction thereof, compared to a control sample, wherein the biomarker is at least one of Apolipoprotein C-IV (“ApoC-IV”), Paraoxonase 1 (“PON-1”), Complement Factor 3 (“C3”), Apolipoprotein A-IV (“ApoA-IV”), Apolipoprotein E (“ApoE”), Apolipoprotein LI (“ApoL 1”), Complement Factor C4 (“C4”), Complement Factor C4B1 (“C4B1”), Histone H2A, Apolipoprotein C-II (“ApoC-II”), Apolipoprotein M (“ApoM”), Vitronectin, Haptoglobin-related Protein and Clusterin. The document also discloses a method for detecting the presence of one or more atherosclerotic lesions wherein a change in the amount of a biomarker in the biological sample or HDL sub-fraction thereof is detected, compared to a control sample and wherein the biomarker is selected from PON-1, C3, C4, ApoE, ApoM and C4B1. All biomarkers mentioned in this document are protein or lipoprotein biomarkers.
WO2011/063470 compares the lipid profiles of patients with coronary disease (stable) with patients with acute coronary syndrome (ACS) having acute chest pain, ECG changes and troponin I elevations. This comparison revealed lipid markers that associate with troponin I and clinical markers of ACS suggesting that lipids may be used as a biomarker of acute myocardial ischemia. However, in acute cardiovascular setting, troponin I seems to be superior marker compared to lipid profiles (Meikle et al. Plasma lipidomic analysis of stable and unstable coronary artery disease. Arterioscler Thromb Vasc Biol. 2011 November; 31(11):2723-32.) and the findings do not predict patient outcome nor long-term risk of acute myocardial ischemia or cardiovascular death.
From previous work it cannot be extrapolated that lipid analysis will yield by default a CVD biomarker predictive to the fatal outcomes associated with CVD/CAD. There remains a need for specific markers useful for identifying specific risk patient populations within patients generally suffering from or being at risk of CVD/CAD.
The present invention identifies biomarkers of high risk CVD by absolute, or close to absolute, quantification of defined molecular lipid species instead of profiling multiple analytes. Importantly, while many of the existing biomarker candidates are composite fingerprints of multiple factors, the lipidomics approach herein shows value already at a level of single species or ratios thereof. The present application discloses an improved lipid assay approach over those in the prior art since it takes into account factors that affect lipid metabolism such as lipid lowering treatment (e.g. statins) and diabetes. Therefore, the present application provides novel personalized prediction markers.